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New Perspectives on Game-Based Assessment with Process Data and Physiological Signals

  • Steve Nebel
  • Manuel NinausEmail author
Chapter
Part of the Advances in Game-Based Learning book series (AGBL)

Abstract

The unfolding empowerment of instructors as game designers with approachable and widely available tools such as Scratch, Minecraft, or Unreal Engine shifted the perspective on game-based assessment (GBA). An increasing number of instructors are capable of creating games themselves, subsequently gaining access to the mechanics and the embedded data. Thus, detailed information regarding each individual player becomes accessible. This gains importance, as this approach might amplify the availability of desperately needed process data within the fields of instructional psychology and game-based learning research. However, this approach is still in its infancy, and future users and researchers need guidance regarding the gathering and the interpretation of insights created by complex process data within GBA. This becomes particularly important with the increasing use of physiological data in learning as well as assessment scenarios. The ubiquitous availability of sensors acquiring physiological data allows for new and noninvasive ways of acquiring objective real-time data that can provide deeper insights into emotional and cognitive states of players. As the technology becomes less expensive and increasingly novice-friendly, more applications are emerging, ranging from GBA within sport applications to scientific experiments that attempt to connect physiological measures to psychological concepts. This chapter highlights the potentials of process, as well as physiological data, but also the problems that can arise in this context. Finally, this chapter aims to provide a new perspective on the emerging trend of using such data within GBA.

Keywords

Process data Physiological data Data analysis Educational video games Instructional psychology Adaptivity 

Notes

Acknowledgments

The current research was funded by the Leibniz-Competition Fund (SAW-2016-IWM-3) and the Leibniz-WissenschaftsCampus “Cognitive Interfaces” (MWK-WCT TP12) supporting Manuel Ninaus.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technische Universität ChemnitzChemnitzGermany
  2. 2.Leibniz-Institut für WissensmedienTübingenGermany

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